Moving human-computer interaction off the desktop and into our cities
requires new approaches to understanding people and technologies in the built
environment. We approach the city as a system, with human, physical and digital
components and behaviours. In creating effective and usable urban pervasive
computing systems, we need to take into account the patterns of movement and
encounter amongst people, locations, and mobile and fixed devices in the city.
Advances in mobile and wireless communications have enabled us to detect and
record the presence and movement of devices through cities. This article makes
a number of methodological and empirical contributions. We present a toolkit of
algorithms and visualization techniques that we have developed to model and
make sense of spatial and temporal patterns of mobility, presence, and
encounter. Applying this toolkit, we provide an analysis of urban Bluetooth
data based on a longitudinal dataset containing millions of records associated
with more than 70000 unique devices in the city of Bath, UK. Through a novel
application of established complex network analysis techniques, we demonstrate
a significant finding on the relationship between temporal factors and network
structure. Finally, we suggest how our understanding and exploitation of these
data may begin to inform the design and use of urban pervasive systems.

Can we learn about users' problem-solving strategies by observing their
actions? This article introduces a data mining system that extracts complex
behavioral patterns from logged user actions to discover users' high-level
strategies. Our application domain is an HCI study aimed at revealing users'
strategies in an end-user debugging task and understanding how the strategies
relate to gender and to success. We cast this problem as a sequential pattern
discovery problem, where user strategies are manifested as sequential behavior
patterns. Problematically, we found that the patterns discovered by standard
data mining algorithms were difficult to interpret and provided limited
information about high-level strategies. To help interpret the patterns as
strategies, we examined multiple ways of clustering the patterns into
meaningful groups. This collectively led to interesting findings about users'
behavior in terms of both gender differences and debugging success. These
common behavioral patterns were novel HCI findings about differences in males'
and females' behavior with software, and were verified by a parallel study with
an independent data set on strategies. As a research endeavor into the
interpretability issues faced by data mining techniques, our work also
highlights important research directions for making data mining more accessible
to non-data-mining experts.

Current Web search tools do a good job of retrieving documents that satisfy
the most common intentions associated with a query, but do not do a very good
job of discerning different individuals' unique search goals. We explore the
variation in what different people consider relevant to the same query by
mining three data sources: (1) explicit relevance judgments, (2) clicks on
search results (a behavior-based implicit measure of relevance), and (3) the
similarity of desktop content to search results (a content-based implicit
measure of relevance). We find that people's explicit judgments for the same
queries differ greatly. As a result, there is a large gap between how well
search engines could perform if they were to tailor results to the individual,
and how well they currently perform by returning results designed to satisfy
everyone. We call this gap the potential for personalization. The two implicit
indicators we studied provide complementary value for approximating this
variation in result relevance among people. We discuss several uses of our
findings, including a personalized search system that takes advantage of the
implicit measures by ranking personally relevant results more highly and
improving click-through rates.

Experiments on the preference-based organization interface in recommender
systems

As e-commerce has evolved into its second generation, where the available
products are becoming more complex and their abundance is almost unlimited, the
task of locating a desired choice has become too difficult for the average
user. Therefore, more effort has been made in recent years to develop
recommender systems that recommend products or services to users so as to
assist in their decision-making process. In this article, we describe crucial
experimental results about a novel recommender technology, called the
preference-based organization (Pref-ORG), which generates critique suggestions
in addition to recommendations according to users' preferences. The critique is
a form of feedback ("I would like something cheaper than this one") that users
can provide to the currently displayed product, with which the system may
better predict what the user truly wants. We compare the preference-based
organization technique with related approaches, including the ones that also
produce critique candidates, but without the consideration of user preferences.
A simulation setup is first presented, that identified Pref-ORG's significantly
higher algorithm accuracy in predicting critiques and choices that users should
intend to make, followed by a real-user evaluation which practically verified
its significant impact on saving users' decision effort.

TOCHI 2010 Volume 17 Issue 2

When information is known only to friends in a social network, traditional
crowdsourcing mechanisms struggle to motivate a large enough user population
and to ensure accuracy of the collected information. We thus introduce
friendsourcing, a form of crowdsourcing aimed at collecting accurate
information available only to a small, socially-connected group of individuals.
Our approach to friendsourcing is to design socially enjoyable interactions
that produce the desired information as a side effect.
We focus our analysis around Collabio, a novel social tagging game that we
developed to encourage friends to tag one another within an online social
network. Collabio encourages friends, family, and colleagues to generate useful
information about each other. We describe the design space of incentives in
social tagging games and evaluate our choices by a combination of usage log
analysis and survey data. Data acquired via Collabio is typically accurate and
augments tags that could have been found on Facebook or the Web. To complete
the arc from data collection to application, we produce a trio of prototype
applications to demonstrate how Collabio tags could be utilized: an aggregate
tag cloud visualization, a personalized RSS feed, and a question and answer
system. The social data powering these applications enables them to address
needs previously difficult to support, such as question answering for topics
comprehensible only to a few of a user's friends.

We investigated the effects of facial similarity between users and embodied
agents under different experimental conditions. Sixty-four undergraduates
interacted with two different embodied agents: in one case the agent was
designed to look somewhat similar to the user, and in the other case the agent
was designed to look dissimilar. We varied between subjects how helpful the
agent was for a given task. Results showed that the facial similarity
manipulation sometimes affected participants' responses, even though they did
not consciously detect the similarity. Specifically, when the agent was
helpful, facial similarity increased participants' ratings of involvement.
However, when exposed to unhelpful agents, male participants had negative
responses to the similar-looking agent compared to the dissimilar one. These
results suggest that using facially similar embodied agents has a potential
large downside if that embodied agent is perceived to be unhelpful.

An almost explosive growth of complexity puts pressure on people in their
everyday doings. Digital artifacts and systems are at the core of this
development. How should we handle complexity aspects when designing new
interactive devices and systems? In this article we begin an analysis of
interaction complexity. We portray different views of complexity; we explore
not only negative aspects of complexity, but also positive, making a case for
the existence of benign complexity. We argue that complex interaction is not
necessarily bad, but designers need a deeper understanding of interaction
complexity and need to treat it in a more intentional and thoughtful way. We
examine interaction complexity as it relates to different loci of complexity:
internal, external, and mediated complexity. Our purpose with these analytical
exercises is to pave the way for design that is informed by a more focused and
precise understanding of interaction complexity.

Foundations for designing and evaluating user interfaces based on the
crossing paradigm

Traditional graphical user interfaces have been designed with the desktop
mouse in mind, a device well characterized by Fitts' law. Yet in recent years,
hand-held devices and tablet personal computers using a pen (or fingers) as the
primary mean of interaction have become more and more popular. These new
interaction modalities have pushed the traditional focus on pointing to its
limit. In this paper we explore whether a different paradigm -- goal
crossing-based on pen strokes -- may substitute or complement pointing as
another fundamental interaction method. First we describe a study in which we
establish that goal crossing is dependent on an index of difficulty analogous
to Fitts' law, and that in some settings, goal crossing completion time is
shorter or comparable to pointing performance under the same index of
difficulty. We then demonstrate the expressiveness of the crossing-based
interaction paradigm by implementing CrossY, an application which only uses
crossing for selecting commands. CrossY demonstrates that crossing-based
interactions can be more expressive than the standard point and click approach.
We also show how crossing-based interactions encourage the fluid composition of
commands. Finally after observing that users' performance could be influenced
by the general direction of travel, we report on the results of a study
characterizing this effect. These latter results led us to propose a general
guideline for dialog box interaction. Together, these results provide the
foundation for the design of effective crossing-based interactions.

TOCHI 2010 Volume 17 Issue 3

On human remains: Values and practice in the home archiving of cherished
objects

Creating digital archives of personal and family artifacts is an area of
growing interest, but which seemingly is often not supported by a thorough
understanding of current home archiving practice. In this article we seek to
excavate the home archive, exploring those things that people choose to keep
rather than simply accumulate. Based on extensive field research in family
homes we present an investigation of the kinds of sentimental objects, both
physical and digital, to be found in homes, and through in-depth interviews
with family members we explore the values behind archiving practices,
explaining why and how sentimental artefacts are kept. In doing this we wish to
highlight the polysemous nature of things and to argue that archiving practice
in the home is not solely concerned with the invocation of memory. In support
of this we show how sentimental artifacts are also used to connect with others,
to define the self and the family, to fulfill obligations and, quite conversely
to efforts of remembering, to safely forget. Such values are fundamental to
family life where archiving takes place and consequently we explore how home
archiving is achieved as a familial practice in the negotiated spaces of the
home. From this grounded understanding of existing practices and values, in
context, we derive requirements and implications for the design of future forms
of domestic archiving technology.

The design and evaluation of a scanning ambiguous keyboard (SAK) is
presented. SAK combines the most demanding requirement of a scanning keyboard
-- input using one key or switch -- with the most appealing feature of an
ambiguous keyboard -- one key press per letter. The optimal design requires
just 1.713 scan steps per character for English text entry. In a provisional
evaluation, 12 able-bodied participants each entered 5 blocks of text with the
scanning interval decreasing from 1100 ms initially to 700 ms at the end. The
average text entry rate in the 5th block was 5.11 wpm with 99% accuracy. One
participant performed an additional five blocks of trials and reached an
average speed of 9.28 wpm on the 10th block. Afterwards, the usefulness of the
approach for persons with severe physical disabilities was shown in a case
study with a software implementation of the idea explicitly adapted for that
target community.

We present a semantic imitation model of social tagging and exploratory
search based on theories of cognitive science. The model assumes that social
tags evoke a spontaneous tag-based topic inference process that primes the
semantic interpretation of resource contents during exploratory search, and the
semantic priming of existing tags in turn influences future tag choices. The
model predicts that (1) users who can see tags created by others tend to create
tags that are semantically similar to these existing tags, demonstrating the
social influence of tag choices; and (2) users who have similar information
goals tend to create tags that are semantically similar, but this effect is
mediated by the semantic representation and interpretation of social tags.
Results from the experiment comparing tagging behavior between a social group
(where participants can see tags created by others) and a nominal group (where
participants cannot see tags created by others) confirmed these predictions.
The current results highlight the critical role of human semantic
representations and interpretation processes in the analysis of large-scale
social information systems. The model implies that analysis at both the
individual and social levels are important for understanding the active,
dynamic processes between human knowledge structures and external folksonomies.
Implications on how social tagging systems can facilitate exploratory search,
interactive information retrievals, knowledge exchange, and other higher-level
cognitive and learning activities are discussed.

A model of novice and expert navigation performance in constrained-input
interfaces

Many interactive systems require users to navigate through large sets of
data and commands using constrained input devices -- such as scroll rings,
rocker switches, or specialized keypads -- that provide less power and
flexibility than traditional input devices like mice or touch screens. While
performance with more traditional devices has been extensively studied in
human-computer interaction, there has been relatively little investigation of
human performance with constrained input. As a result, there is little
understanding of what factors govern performance in these situations, and how
interfaces should be designed to optimize interface actions such as navigation
and selection. Since constrained input is now common in a wide variety of
interactive systems (such as mobile phones, audio players, in-car navigation
systems, and kiosk displays), it is important for designers to understand what
factors affect performance. To aid in this understanding, we present the
Constrained Input Navigation (CIN) model, a predictive model that allows
accurate determination of human navigation and selection performance in
constrained-input scenarios. CIN identifies three factors that underlie user
efficiency: the performance of the interface type for single-level item
selection (where interface type depends on the input and output devices, the
interactive behavior, and the data organization), the hierarchical structure of
the information space, and the user's experience with the items to be selected.
We show through experiments that, after empirical calibration, the model's
predictions fit empirical data well, and discuss why and how each of the
factors affects performance. Models like CIN can provide valuable theoretical
and practical benefits to designers of constrained-input systems, allowing them
to explore and compare a much wider variety of alternate interface designs
without the need for extensive user studies.

TOCHI 2010 Volume 17 Issue 4

A lot of research has been done within the area of mobile computing and
context-awareness over the last 15 years, and the idea of systems adapting to
their context has produced promising results for overcoming some of the
challenges of user interaction with mobile devices within various specialized
domains. However, today it is still the case that only a limited body of
theoretically grounded knowledge exists that can explain the relationship
between users, mobile system user interfaces, and their context. Lack of such
knowledge limits our ability to elevate learning from the mobile systems we
develop and study from a concrete to an abstract level. Consequently, the
research field is impeded in its ability to leap forward and is limited to
incremental steps from one design to the next. Addressing the problem of this
void, this article contributes to the body of knowledge about mobile
interaction design by promoting a theoretical approach for describing and
understanding the relationship between user interface representations and user
context. Specifically, we promote the concept of indexicality derived from
semiotics as an analytical concept that can be used to describe and understand
a design. We illustrate the value of the indexicality concept through an
analysis of empirical data from evaluations of three prototype systems in use.
Based on our analytical and empirical work we promote the view that users
interpret information in a mobile computer user interface through creation of
meaningful indexical signs based on the ensemble of context and system.

Oasis: A framework for linking notification delivery to the perceptual
structure of goal-directed tasks

A notification represents the proactive delivery of information to a user
and reduces the need to visually scan or repeatedly check an external
information source. At the same time, notifications often interrupt user tasks
at inopportune moments, decreasing productivity and increasing frustration.
Controlled studies have shown that linking notification delivery to the
perceptual structure of a user's tasks can reduce these interruption costs.
However, in these studies, the scheduling was always performed manually, and it
was not clear whether it would be possible for a system to mimic similar
techniques. This article contributes the design and implementation of a novel
system called Oasis that aligns notification scheduling with the perceptual
structure of user tasks. We describe the architecture of the system, how it
detects task structure on the fly without explicit knowledge of the task
itself, and how it layers flexible notification scheduling policies on top of
this detection mechanism. The system also includes an offline tool for creating
customized statistical models for detecting task structure. The value of our
system is that it intelligently schedules notifications, enabling the
reductions in interruption costs shown within prior controlled studies to now
be realized by users in everyday desktop computing tasks. It also provides a
test bed for experimenting with how notification management policies and other
system functionalities can be linked to task structure.

Internet-based video delivery offers new opportunities for interactive
television. The creation and usability of interactive television is very
different from desktop or web-based interaction. The concepts of frameworks and
genres provides an approach to learnable interaction in an entertainment rather
than task-oriented activity. The concept of a framework defines the tools
required for both producing and viewing a particular style of interactive video
experience. An interactive framework for televised sports is presented. This
framework implements a sports television experience that support play-by-play
navigation as well as viewer's interactive choice of camera angles. Tools for
creating and viewing interactive sports are developed in parallel. In-home and
in-lab experiments give indications of how sports fans will use interactive
television in the future. The experiments demonstrate that fans will use the
interaction rather than passively watching, can easily learn the interactive
features and strongly prefer the new features over tradition
rewind/fast-forward. The data indicates that many users will use the
interactive controls to enrich and prolong their viewing rather than simply
skipping as rapidly as possible through a game. However, there is also
indication that some viewers will simply skip rapidly. There are also
indications that the skip vs. review interaction depends on the interest level
of current game play.

Mobile product recommendation agents (RAs) are software systems that operate
on mobile handheld devices, using wireless Internet to support users' decisions
en route, such as consumers' product choices in retail stores. As the demand
for ubiquitous access to the web grows, potential benefits of mobile RAs have
been recognized, albeit with little supporting empirical evidence. We
investigate whether and how mobile RAs enhance users' decisions in retail
stores by reducing the effort to make purchase decisions while augmenting the
accuracy of the decisions. In addition, to identify potential design principles
for mobile RAs, we compare and evaluate two interaction styles of mobile RAs:
alternative-driven (RA-AL) versus attribute-driven (RA-AT) interactions. The
results of a laboratory experiment conducted in a simulated store indicate that
mobile RAs reduced users' perceived effort and increased accuracy of their
decisions. Furthermore, RA-AL users made more accurate decisions than RA-AT
users due to the RA-AL's interaction style, which was compatible with the way
in which users processed information and made decisions in the store. These
empirical results support the notion that mobile RAs should be designed to fit
the user's task undertaken in the particular context.

Iteration can help people improve ideas. It can also give rise to fixation,
continuously refining one option without considering others. Does creating and
receiving feedback on multiple prototypes in parallel, as opposed to serially,
affect learning, self-efficacy, and design exploration? An experiment
manipulated whether independent novice designers created graphic Web
advertisements in parallel or in series. Serial participants received
descriptive critique directly after each prototype. Parallel participants
created multiple prototypes before receiving feedback. As measured by
click-through data and expert ratings, ads created in the Parallel condition
significantly outperformed those from the Serial condition. Moreover,
independent raters found Parallel prototypes to be more diverse. Parallel
participants also reported a larger increase in task-specific self-confidence.
This article outlines a theoretical foundation for why parallel prototyping
produces better design results and discusses the implications for design
education.